
import os
import cv2
import numpy as np
import pandas as pd
import pickle
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from tqdm import tqdm


from plotly.offline import init_notebook_mode
init_notebook_mode(connected=True)


# Importing Base class Tensorflow
import tensorflow as tf
print("Tensorflow Version => ",tf.__version__)

Tensorflow Version =>  2.4.0


import plotly.offline as pyo
pyo.init_notebook_mode()


label_dir = "dataset/labels.csv"
datadir = "dataset/"
target_shape = [128,128,3]
height,width,depth = target_shape

train_batch_size = 6
val_batch_size = 4

latent_dim = 100


labels_file = pd.read_csv(label_dir,index_col="frame")
all_files = os.listdir(datadir)


images = []
labels = []


# Iterating through all files
for file in all_files:
    # check wether it is not csv file
    if not file.endswith(".csv"):
        # go through all images -> laod image-> reshape image 
        for image_name in os.listdir(os.path.join(datadir,file)):
            #Load image
            image_path = os.path.join(datadir,file) + "/" + image_name
            img = cv2.imread(image_path)
            # Resize Image
            img = cv2.resize(img, (height,width))
            
            # storing images and labels
            images.append(img)
            labels.append(labels_file.loc[file[:-6]]["label"])


# Converting list to numpy array
images = np.array(images)
labels = np.array(labels)


label_enc = OneHotEncoder()
labels_arr = label_enc.fit_transform(labels.reshape(-1,1)).toarray()


#Shuffling indices
indices = np.arange(images.shape[0])
np.random.shuffle(indices)
# Shuffling images and labels
images = images[indices]
labels = labels[indices]
labels_arr = labels_arr[indices]


nrows = 3
ncols = 4

fig, axes = plt.subplots(nrows=nrows,ncols = ncols)
random_idx = np.random.randint(0,len(images),nrows*ncols)
fig.set_size_inches(16,12)


for idx,i in enumerate(random_idx):
    image = images[i]
    
    plt.subplot(nrows,ncols,idx+1)
    plt.imshow(image)
    plt.title(labels[i])
    plt.axis('off')
plt.subplots_adjust(left = 0,wspace = 0.5,hspace = 0)
plt.suptitle("Sample Data")
plt.show()


# Data with  preprocessing
img_gen = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range = 40,
                                                                   horizontal_flip=True,
                                                                   shear_range = 0.2,
                                                                   zoom_range = 0.2,
                                                                   rescale=1/255,
                                                                   validation_split=0.2)


train_data = img_gen.flow(images,labels_arr, batch_size=train_batch_size,subset='training')

val_data = img_gen.flow(images,labels_arr,batch_size=val_batch_size, subset='validation')


nrows = 2
ncols = 2

fig, axes = plt.subplots(nrows=nrows,ncols = ncols)
fig.set_size_inches(10,12)

temp_img = train_data.next()
for i in range(nrows*ncols):
    image = temp_img[0][i]
    
    plt.subplot(nrows,ncols,i+1)
    plt.imshow(image)
    plt.title(temp_img[1][i])
    plt.axis('off')
plt.subplots_adjust(left = 0,wspace = 0,hspace = 0.2)
plt.suptitle("Data with Augmentation")
plt.show()

C:\Users\soura\anaconda3\lib\site-packages\matplotlib\text.py:1165: FutureWarning:

elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison


# Defining model 
# We will be training from scratch
model = tf.keras.applications.InceptionV3(include_top=False, weights='imagenet', input_shape=target_shape)

model_InceptionV3 = tf.keras.models.Sequential()
model_InceptionV3.add(model)
model_InceptionV3.add(tf.keras.layers.Flatten())
model_InceptionV3.add(tf.keras.layers.BatchNormalization())
model_InceptionV3.add(tf.keras.layers.Dense(256, activation='relu'))
model_InceptionV3.add(tf.keras.layers.Dropout(0.5))
model_InceptionV3.add(tf.keras.layers.BatchNormalization())
model_InceptionV3.add(tf.keras.layers.Dense(128, activation='relu'))
model_InceptionV3.add(tf.keras.layers.Dropout(0.5))
model_InceptionV3.add(tf.keras.layers.BatchNormalization())
model_InceptionV3.add(tf.keras.layers.Dense(3, activation='softmax'))

model_InceptionV3.layers[0].trainable=False


model_InceptionV3.compile(optimizer="adam",loss="categorical_crossentropy",metrics=["accuracy",
                                                                                    tf.keras.metrics.Precision(top_k=1),
                                                                                    tf.keras.metrics.Recall(top_k=1)])

history_model_InceptionV3 = model_InceptionV3.fit(train_data,batch_size=train_batch_size,validation_data=val_data,epochs=50)

Epoch 1/50
214/214 [==============================] - 34s 100ms/step - loss: 1.3102 - accuracy: 0.4398 - precision: 0.4398 - recall: 0.4398 - val_loss: 0.8677 - val_accuracy: 0.6406 - val_precision: 0.6406 - val_recall: 0.6406
Epoch 2/50
214/214 [==============================] - 17s 81ms/step - loss: 1.0006 - accuracy: 0.5384 - precision: 0.5384 - recall: 0.5384 - val_loss: 0.7718 - val_accuracy: 0.6687 - val_precision: 0.6687 - val_recall: 0.6687
Epoch 3/50
214/214 [==============================] - 17s 81ms/step - loss: 0.9551 - accuracy: 0.5493 - precision: 0.5493 - recall: 0.5493 - val_loss: 0.7024 - val_accuracy: 0.6875 - val_precision: 0.6875 - val_recall: 0.6875
Epoch 4/50
214/214 [==============================] - 18s 82ms/step - loss: 0.9358 - accuracy: 0.5497 - precision: 0.5497 - recall: 0.5497 - val_loss: 0.6841 - val_accuracy: 0.6781 - val_precision: 0.6781 - val_recall: 0.6781
Epoch 5/50
214/214 [==============================] - 19s 90ms/step - loss: 0.8706 - accuracy: 0.5965 - precision: 0.5965 - recall: 0.5965 - val_loss: 0.6568 - val_accuracy: 0.7156 - val_precision: 0.7156 - val_recall: 0.7156
Epoch 6/50
214/214 [==============================] - 21s 97ms/step - loss: 0.8410 - accuracy: 0.5886 - precision: 0.5886 - recall: 0.5886 - val_loss: 0.6272 - val_accuracy: 0.7188 - val_precision: 0.7188 - val_recall: 0.7188
Epoch 7/50
214/214 [==============================] - 18s 84ms/step - loss: 0.8138 - accuracy: 0.6121 - precision: 0.6121 - recall: 0.6121 - val_loss: 0.6073 - val_accuracy: 0.7437 - val_precision: 0.7437 - val_recall: 0.7437
Epoch 8/50
214/214 [==============================] - 19s 87ms/step - loss: 0.7976 - accuracy: 0.6159 - precision: 0.6159 - recall: 0.6159 - val_loss: 0.5803 - val_accuracy: 0.7219 - val_precision: 0.7219 - val_recall: 0.7219
Epoch 9/50
214/214 [==============================] - 25s 118ms/step - loss: 0.7844 - accuracy: 0.6075 - precision: 0.6075 - recall: 0.6075 - val_loss: 0.5705 - val_accuracy: 0.7594 - val_precision: 0.7594 - val_recall: 0.7594
Epoch 10/50
214/214 [==============================] - 27s 125ms/step - loss: 0.8039 - accuracy: 0.6277 - precision: 0.6277 - recall: 0.6277 - val_loss: 0.5448 - val_accuracy: 0.7812 - val_precision: 0.7812 - val_recall: 0.7812
Epoch 11/50
214/214 [==============================] - 21s 100ms/step - loss: 0.7287 - accuracy: 0.6725 - precision: 0.6725 - recall: 0.6725 - val_loss: 0.5600 - val_accuracy: 0.7312 - val_precision: 0.7312 - val_recall: 0.7312
Epoch 12/50
214/214 [==============================] - 19s 89ms/step - loss: 0.7669 - accuracy: 0.6472 - precision: 0.6472 - recall: 0.6472 - val_loss: 0.5040 - val_accuracy: 0.7719 - val_precision: 0.7719 - val_recall: 0.7719
Epoch 13/50
214/214 [==============================] - 18s 84ms/step - loss: 0.7334 - accuracy: 0.6596 - precision: 0.6596 - recall: 0.6596 - val_loss: 0.4510 - val_accuracy: 0.8219 - val_precision: 0.8219 - val_recall: 0.8219
Epoch 14/50
214/214 [==============================] - 17s 79ms/step - loss: 0.7247 - accuracy: 0.6446 - precision: 0.6446 - recall: 0.6446 - val_loss: 0.5062 - val_accuracy: 0.7969 - val_precision: 0.7969 - val_recall: 0.7969
Epoch 15/50
214/214 [==============================] - 17s 78ms/step - loss: 0.7459 - accuracy: 0.6634 - precision: 0.6634 - recall: 0.6634 - val_loss: 0.4793 - val_accuracy: 0.8281 - val_precision: 0.8281 - val_recall: 0.8281
Epoch 16/50
214/214 [==============================] - 19s 90ms/step - loss: 0.7352 - accuracy: 0.6584 - precision: 0.6584 - recall: 0.6584 - val_loss: 0.4488 - val_accuracy: 0.8188 - val_precision: 0.8188 - val_recall: 0.8188
Epoch 17/50
214/214 [==============================] - 23s 107ms/step - loss: 0.7242 - accuracy: 0.6844 - precision: 0.6844 - recall: 0.6844 - val_loss: 0.4568 - val_accuracy: 0.8094 - val_precision: 0.8094 - val_recall: 0.8094
Epoch 18/50
214/214 [==============================] - 16s 75ms/step - loss: 0.6599 - accuracy: 0.7024 - precision: 0.7024 - recall: 0.7024 - val_loss: 0.4872 - val_accuracy: 0.7969 - val_precision: 0.7969 - val_recall: 0.7969
Epoch 19/50
214/214 [==============================] - 23s 106ms/step - loss: 0.7022 - accuracy: 0.6751 - precision: 0.6751 - recall: 0.6751 - val_loss: 0.4456 - val_accuracy: 0.7937 - val_precision: 0.7937 - val_recall: 0.7937
Epoch 20/50
214/214 [==============================] - 19s 88ms/step - loss: 0.7142 - accuracy: 0.6922 - precision: 0.6922 - recall: 0.6922 - val_loss: 0.4419 - val_accuracy: 0.8313 - val_precision: 0.8313 - val_recall: 0.8313
Epoch 21/50
214/214 [==============================] - 20s 93ms/step - loss: 0.7155 - accuracy: 0.6690 - precision: 0.6690 - recall: 0.6690 - val_loss: 0.3997 - val_accuracy: 0.8313 - val_precision: 0.8313 - val_recall: 0.8313
Epoch 22/50
214/214 [==============================] - 20s 94ms/step - loss: 0.7055 - accuracy: 0.6859 - precision: 0.6859 - recall: 0.6859 - val_loss: 0.4070 - val_accuracy: 0.8406 - val_precision: 0.8406 - val_recall: 0.8406
Epoch 23/50
214/214 [==============================] - 16s 77ms/step - loss: 0.6592 - accuracy: 0.6996 - precision: 0.6996 - recall: 0.6996 - val_loss: 0.3752 - val_accuracy: 0.8656 - val_precision: 0.8656 - val_recall: 0.8656
Epoch 24/50
214/214 [==============================] - 20s 92ms/step - loss: 0.6674 - accuracy: 0.6909 - precision: 0.6909 - recall: 0.6909 - val_loss: 0.4183 - val_accuracy: 0.8406 - val_precision: 0.8406 - val_recall: 0.8406
Epoch 25/50
214/214 [==============================] - 21s 98ms/step - loss: 0.6467 - accuracy: 0.7107 - precision: 0.7107 - recall: 0.7107 - val_loss: 0.3695 - val_accuracy: 0.8562 - val_precision: 0.8562 - val_recall: 0.8562
Epoch 26/50
214/214 [==============================] - 19s 88ms/step - loss: 0.6405 - accuracy: 0.7029 - precision: 0.7029 - recall: 0.7029 - val_loss: 0.3552 - val_accuracy: 0.8594 - val_precision: 0.8594 - val_recall: 0.8594
Epoch 27/50
214/214 [==============================] - 19s 87ms/step - loss: 0.6913 - accuracy: 0.6961 - precision: 0.6961 - recall: 0.6961 - val_loss: 0.3855 - val_accuracy: 0.8500 - val_precision: 0.8500 - val_recall: 0.8500
Epoch 28/50
214/214 [==============================] - 19s 89ms/step - loss: 0.6211 - accuracy: 0.7298 - precision: 0.7298 - recall: 0.7298 - val_loss: 0.4511 - val_accuracy: 0.7937 - val_precision: 0.7937 - val_recall: 0.7937
Epoch 29/50
214/214 [==============================] - 21s 99ms/step - loss: 0.6327 - accuracy: 0.7242 - precision: 0.7242 - recall: 0.7242 - val_loss: 0.3950 - val_accuracy: 0.8531 - val_precision: 0.8531 - val_recall: 0.8531
Epoch 30/50
214/214 [==============================] - 18s 83ms/step - loss: 0.6366 - accuracy: 0.7180 - precision: 0.7180 - recall: 0.7180 - val_loss: 0.3333 - val_accuracy: 0.8813 - val_precision: 0.8813 - val_recall: 0.8813
Epoch 31/50
214/214 [==============================] - 18s 83ms/step - loss: 0.6378 - accuracy: 0.7213 - precision: 0.7213 - recall: 0.7213 - val_loss: 0.3534 - val_accuracy: 0.8687 - val_precision: 0.8687 - val_recall: 0.8687
Epoch 32/50
214/214 [==============================] - 19s 89ms/step - loss: 0.6599 - accuracy: 0.7176 - precision: 0.7176 - recall: 0.7176 - val_loss: 0.3493 - val_accuracy: 0.8531 - val_precision: 0.8531 - val_recall: 0.8531
Epoch 33/50
214/214 [==============================] - 17s 81ms/step - loss: 0.6360 - accuracy: 0.7349 - precision: 0.7349 - recall: 0.7349 - val_loss: 0.3147 - val_accuracy: 0.8781 - val_precision: 0.8781 - val_recall: 0.8781
Epoch 34/50
214/214 [==============================] - 17s 81ms/step - loss: 0.6304 - accuracy: 0.7050 - precision: 0.7050 - recall: 0.7050 - val_loss: 0.3362 - val_accuracy: 0.8406 - val_precision: 0.8406 - val_recall: 0.8406
Epoch 35/50
214/214 [==============================] - 17s 78ms/step - loss: 0.6147 - accuracy: 0.7205 - precision: 0.7205 - recall: 0.7205 - val_loss: 0.3061 - val_accuracy: 0.8875 - val_precision: 0.8875 - val_recall: 0.8875
Epoch 36/50
214/214 [==============================] - 20s 95ms/step - loss: 0.6424 - accuracy: 0.7176 - precision: 0.7176 - recall: 0.7176 - val_loss: 0.3545 - val_accuracy: 0.8625 - val_precision: 0.8625 - val_recall: 0.8625
Epoch 37/50
214/214 [==============================] - 18s 82ms/step - loss: 0.5881 - accuracy: 0.7398 - precision: 0.7398 - recall: 0.7398 - val_loss: 0.3455 - val_accuracy: 0.8594 - val_precision: 0.8594 - val_recall: 0.8594
Epoch 38/50
214/214 [==============================] - 19s 87ms/step - loss: 0.6344 - accuracy: 0.7324 - precision: 0.7324 - recall: 0.7324 - val_loss: 0.4062 - val_accuracy: 0.8500 - val_precision: 0.8500 - val_recall: 0.8500
Epoch 39/50
214/214 [==============================] - 16s 73ms/step - loss: 0.5618 - accuracy: 0.7606 - precision: 0.7606 - recall: 0.7606 - val_loss: 0.3089 - val_accuracy: 0.8844 - val_precision: 0.8844 - val_recall: 0.8844
Epoch 40/50
214/214 [==============================] - 18s 84ms/step - loss: 0.6110 - accuracy: 0.7275 - precision: 0.7275 - recall: 0.7275 - val_loss: 0.3199 - val_accuracy: 0.8875 - val_precision: 0.8875 - val_recall: 0.8875
Epoch 41/50
214/214 [==============================] - 18s 82ms/step - loss: 0.6165 - accuracy: 0.7433 - precision: 0.7433 - recall: 0.7433 - val_loss: 0.3767 - val_accuracy: 0.8531 - val_precision: 0.8531 - val_recall: 0.8531
Epoch 42/50
214/214 [==============================] - 21s 100ms/step - loss: 0.5576 - accuracy: 0.7562 - precision: 0.7562 - recall: 0.7562 - val_loss: 0.2811 - val_accuracy: 0.9156 - val_precision: 0.9156 - val_recall: 0.9156
Epoch 43/50
214/214 [==============================] - 23s 106ms/step - loss: 0.5814 - accuracy: 0.7449 - precision: 0.7449 - recall: 0.7449 - val_loss: 0.3251 - val_accuracy: 0.8594 - val_precision: 0.8594 - val_recall: 0.8594
Epoch 44/50
214/214 [==============================] - 16s 74ms/step - loss: 0.6081 - accuracy: 0.7245 - precision: 0.7245 - recall: 0.7245 - val_loss: 0.3506 - val_accuracy: 0.8687 - val_precision: 0.8687 - val_recall: 0.8687
Epoch 45/50
214/214 [==============================] - 16s 77ms/step - loss: 0.5690 - accuracy: 0.7577 - precision: 0.7577 - recall: 0.7577 - val_loss: 0.3137 - val_accuracy: 0.8969 - val_precision: 0.8969 - val_recall: 0.8969
Epoch 46/50
214/214 [==============================] - 17s 79ms/step - loss: 0.5741 - accuracy: 0.7558 - precision: 0.7558 - recall: 0.7558 - val_loss: 0.3395 - val_accuracy: 0.8906 - val_precision: 0.8906 - val_recall: 0.8906
Epoch 47/50
214/214 [==============================] - 18s 82ms/step - loss: 0.5773 - accuracy: 0.7502 - precision: 0.7502 - recall: 0.7502 - val_loss: 0.3282 - val_accuracy: 0.8875 - val_precision: 0.8875 - val_recall: 0.8875
Epoch 48/50
214/214 [==============================] - 16s 76ms/step - loss: 0.5534 - accuracy: 0.7549 - precision: 0.7549 - recall: 0.7549 - val_loss: 0.3001 - val_accuracy: 0.8906 - val_precision: 0.8906 - val_recall: 0.8906
Epoch 49/50
214/214 [==============================] - 18s 82ms/step - loss: 0.5966 - accuracy: 0.7505 - precision: 0.7505 - recall: 0.7505 - val_loss: 0.3011 - val_accuracy: 0.8844 - val_precision: 0.8844 - val_recall: 0.8844
Epoch 50/50
214/214 [==============================] - 18s 82ms/step - loss: 0.6254 - accuracy: 0.7274 - precision: 0.7274 - recall: 0.7274 - val_loss: 0.2891 - val_accuracy: 0.9000 - val_precision: 0.9000 - val_recall: 0.9000


custom_model = tf.keras.models.Sequential()

custom_model.add(tf.keras.layers.Conv2D(64, (3, 3), padding='same', input_shape=target_shape, activation='relu'))
custom_model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
custom_model.add(tf.keras.layers.BatchNormalization())

custom_model.add(tf.keras.layers.Conv2D(128, (3, 3), padding='same',activation='relu'))
custom_model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
custom_model.add(tf.keras.layers.BatchNormalization())

custom_model.add(tf.keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))
custom_model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
custom_model.add(tf.keras.layers.BatchNormalization())


custom_model.add(tf.keras.layers.Conv2D(256, (3, 3), padding='same',activation='relu'))
custom_model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))

custom_model.add(tf.keras.layers.Flatten())

custom_model.add(tf.keras.layers.Dense(128, activation='relu',input_dim=128))
custom_model.add(tf.keras.layers.Dropout(0.3))
custom_model.add(tf.keras.layers.BatchNormalization())

custom_model.add(tf.keras.layers.Dense(128, activation='relu'))

custom_model.add(tf.keras.layers.Dense(3, activation='softmax'))


custom_model.compile(optimizer="adam",loss="categorical_crossentropy",metrics=["accuracy",
                                                                               tf.keras.metrics.Precision(top_k=1),
                                                                               tf.keras.metrics.Recall(top_k=1)])

history_custom_model = custom_model.fit(train_data,batch_size=train_batch_size,validation_data=val_data,epochs=50)

Epoch 1/50
214/214 [==============================] - 15s 53ms/step - loss: 0.9768 - accuracy: 0.5050 - precision_1: 0.5050 - recall_1: 0.5050 - val_loss: 1.7910 - val_accuracy: 0.1656 - val_precision_1: 0.1656 - val_recall_1: 0.1656
Epoch 2/50
214/214 [==============================] - 10s 48ms/step - loss: 0.9053 - accuracy: 0.5726 - precision_1: 0.5726 - recall_1: 0.5726 - val_loss: 1.1832 - val_accuracy: 0.2937 - val_precision_1: 0.2937 - val_recall_1: 0.2937
Epoch 3/50
214/214 [==============================] - 10s 47ms/step - loss: 0.7856 - accuracy: 0.6496 - precision_1: 0.6496 - recall_1: 0.6496 - val_loss: 1.1262 - val_accuracy: 0.4969 - val_precision_1: 0.4969 - val_recall_1: 0.4969
Epoch 4/50
214/214 [==============================] - 10s 46ms/step - loss: 0.8381 - accuracy: 0.6192 - precision_1: 0.6192 - recall_1: 0.6192 - val_loss: 0.8198 - val_accuracy: 0.6219 - val_precision_1: 0.6219 - val_recall_1: 0.6219
Epoch 5/50
214/214 [==============================] - 10s 48ms/step - loss: 0.7692 - accuracy: 0.6282 - precision_1: 0.6282 - recall_1: 0.6282 - val_loss: 0.8096 - val_accuracy: 0.6125 - val_precision_1: 0.6125 - val_recall_1: 0.6125
Epoch 6/50
214/214 [==============================] - 11s 49ms/step - loss: 0.7652 - accuracy: 0.6426 - precision_1: 0.6426 - recall_1: 0.6426 - val_loss: 1.5177 - val_accuracy: 0.4156 - val_precision_1: 0.4156 - val_recall_1: 0.4156
Epoch 7/50
214/214 [==============================] - 11s 49ms/step - loss: 0.7408 - accuracy: 0.6447 - precision_1: 0.6447 - recall_1: 0.6447 - val_loss: 3.5200 - val_accuracy: 0.3625 - val_precision_1: 0.3625 - val_recall_1: 0.3625
Epoch 8/50
214/214 [==============================] - 12s 55ms/step - loss: 1.0296 - accuracy: 0.4851 - precision_1: 0.4851 - recall_1: 0.4851 - val_loss: 1.0560 - val_accuracy: 0.4313 - val_precision_1: 0.4313 - val_recall_1: 0.4313
Epoch 9/50
214/214 [==============================] - 11s 49ms/step - loss: 0.9585 - accuracy: 0.5023 - precision_1: 0.5023 - recall_1: 0.5023 - val_loss: 0.7902 - val_accuracy: 0.5656 - val_precision_1: 0.5656 - val_recall_1: 0.5656
Epoch 10/50
214/214 [==============================] - 10s 49ms/step - loss: 0.8984 - accuracy: 0.5652 - precision_1: 0.5652 - recall_1: 0.5652 - val_loss: 0.7225 - val_accuracy: 0.6438 - val_precision_1: 0.6438 - val_recall_1: 0.6438
Epoch 11/50
214/214 [==============================] - 13s 59ms/step - loss: 0.8832 - accuracy: 0.5570 - precision_1: 0.5570 - recall_1: 0.5570 - val_loss: 0.7297 - val_accuracy: 0.6219 - val_precision_1: 0.6219 - val_recall_1: 0.6219
Epoch 12/50
214/214 [==============================] - 11s 51ms/step - loss: 0.8299 - accuracy: 0.5924 - precision_1: 0.5924 - recall_1: 0.5924 - val_loss: 0.7094 - val_accuracy: 0.6219 - val_precision_1: 0.6219 - val_recall_1: 0.6219
Epoch 13/50
214/214 [==============================] - 11s 50ms/step - loss: 0.8660 - accuracy: 0.5635 - precision_1: 0.5635 - recall_1: 0.5635 - val_loss: 0.8576 - val_accuracy: 0.6156 - val_precision_1: 0.6156 - val_recall_1: 0.6156
Epoch 14/50
214/214 [==============================] - 11s 50ms/step - loss: 0.8725 - accuracy: 0.5825 - precision_1: 0.5825 - recall_1: 0.5825 - val_loss: 0.6935 - val_accuracy: 0.6656 - val_precision_1: 0.6656 - val_recall_1: 0.6656
Epoch 15/50
214/214 [==============================] - 11s 50ms/step - loss: 0.8350 - accuracy: 0.5937 - precision_1: 0.5937 - recall_1: 0.5937 - val_loss: 0.6477 - val_accuracy: 0.7219 - val_precision_1: 0.7219 - val_recall_1: 0.7219
Epoch 16/50
214/214 [==============================] - 11s 52ms/step - loss: 0.7908 - accuracy: 0.6192 - precision_1: 0.6192 - recall_1: 0.6192 - val_loss: 0.6328 - val_accuracy: 0.7063 - val_precision_1: 0.7063 - val_recall_1: 0.7063
Epoch 17/50
214/214 [==============================] - 11s 51ms/step - loss: 0.7955 - accuracy: 0.6087 - precision_1: 0.6087 - recall_1: 0.6087 - val_loss: 1.4585 - val_accuracy: 0.6844 - val_precision_1: 0.6844 - val_recall_1: 0.6844
Epoch 18/50
214/214 [==============================] - 11s 53ms/step - loss: 0.7731 - accuracy: 0.6270 - precision_1: 0.6270 - recall_1: 0.6270 - val_loss: 3.9606 - val_accuracy: 0.7000 - val_precision_1: 0.7000 - val_recall_1: 0.7000
Epoch 19/50
214/214 [==============================] - 11s 51ms/step - loss: 0.8032 - accuracy: 0.6327 - precision_1: 0.6327 - recall_1: 0.6327 - val_loss: 0.5960 - val_accuracy: 0.7531 - val_precision_1: 0.7531 - val_recall_1: 0.7531
Epoch 20/50
214/214 [==============================] - 11s 52ms/step - loss: 0.7527 - accuracy: 0.6317 - precision_1: 0.6317 - recall_1: 0.6317 - val_loss: 0.6473 - val_accuracy: 0.6750 - val_precision_1: 0.6750 - val_recall_1: 0.6750
Epoch 21/50
214/214 [==============================] - 11s 51ms/step - loss: 0.7303 - accuracy: 0.6429 - precision_1: 0.6429 - recall_1: 0.6429 - val_loss: 0.6324 - val_accuracy: 0.7000 - val_precision_1: 0.7000 - val_recall_1: 0.7000
Epoch 22/50
214/214 [==============================] - 12s 58ms/step - loss: 0.7211 - accuracy: 0.6705 - precision_1: 0.6705 - recall_1: 0.6705 - val_loss: 0.5742 - val_accuracy: 0.7563 - val_precision_1: 0.7563 - val_recall_1: 0.7563
Epoch 23/50
214/214 [==============================] - 15s 70ms/step - loss: 0.6663 - accuracy: 0.6956 - precision_1: 0.6956 - recall_1: 0.6956 - val_loss: 0.5770 - val_accuracy: 0.7344 - val_precision_1: 0.7344 - val_recall_1: 0.7344
Epoch 24/50
214/214 [==============================] - 16s 76ms/step - loss: 0.7154 - accuracy: 0.6716 - precision_1: 0.6716 - recall_1: 0.6716 - val_loss: 0.7566 - val_accuracy: 0.6687 - val_precision_1: 0.6687 - val_recall_1: 0.6687
Epoch 25/50
214/214 [==============================] - 20s 93ms/step - loss: 0.6583 - accuracy: 0.6845 - precision_1: 0.6845 - recall_1: 0.6845 - val_loss: 0.4493 - val_accuracy: 0.8188 - val_precision_1: 0.8188 - val_recall_1: 0.8188
Epoch 26/50
214/214 [==============================] - 18s 84ms/step - loss: 0.6702 - accuracy: 0.6951 - precision_1: 0.6951 - recall_1: 0.6951 - val_loss: 0.9558 - val_accuracy: 0.5500 - val_precision_1: 0.5500 - val_recall_1: 0.5500
Epoch 27/50
214/214 [==============================] - 18s 83ms/step - loss: 0.6817 - accuracy: 0.6865 - precision_1: 0.6865 - recall_1: 0.6865 - val_loss: 1.4347 - val_accuracy: 0.6250 - val_precision_1: 0.6250 - val_recall_1: 0.6250
Epoch 28/50
214/214 [==============================] - 18s 84ms/step - loss: 0.6936 - accuracy: 0.6841 - precision_1: 0.6841 - recall_1: 0.6841 - val_loss: 0.6106 - val_accuracy: 0.7312 - val_precision_1: 0.7312 - val_recall_1: 0.7312
Epoch 29/50
214/214 [==============================] - 16s 74ms/step - loss: 0.6611 - accuracy: 0.7144 - precision_1: 0.7144 - recall_1: 0.7144 - val_loss: 0.4839 - val_accuracy: 0.8156 - val_precision_1: 0.8156 - val_recall_1: 0.8156
Epoch 30/50
214/214 [==============================] - 16s 73ms/step - loss: 0.6238 - accuracy: 0.7294 - precision_1: 0.7294 - recall_1: 0.7294 - val_loss: 0.4331 - val_accuracy: 0.8375 - val_precision_1: 0.8375 - val_recall_1: 0.8375
Epoch 31/50
214/214 [==============================] - 16s 73ms/step - loss: 0.5643 - accuracy: 0.7487 - precision_1: 0.7487 - recall_1: 0.7487 - val_loss: 0.7649 - val_accuracy: 0.7844 - val_precision_1: 0.7844 - val_recall_1: 0.7844
Epoch 32/50
214/214 [==============================] - 18s 82ms/step - loss: 0.6335 - accuracy: 0.7085 - precision_1: 0.7085 - recall_1: 0.7085 - val_loss: 0.3363 - val_accuracy: 0.8656 - val_precision_1: 0.8656 - val_recall_1: 0.8656
Epoch 33/50
214/214 [==============================] - 17s 82ms/step - loss: 0.6675 - accuracy: 0.7042 - precision_1: 0.7042 - recall_1: 0.7042 - val_loss: 0.5729 - val_accuracy: 0.7437 - val_precision_1: 0.7437 - val_recall_1: 0.7437
Epoch 34/50
214/214 [==============================] - 17s 78ms/step - loss: 0.5744 - accuracy: 0.7593 - precision_1: 0.7593 - recall_1: 0.7593 - val_loss: 0.5279 - val_accuracy: 0.7500 - val_precision_1: 0.7500 - val_recall_1: 0.750068 - accuracy: 0.7676 - precision_1: 0.7676 - recall_1:  - ETA: 3s - loss: 0.5680 - accuracy:
Epoch 35/50
214/214 [==============================] - 16s 73ms/step - loss: 0.5820 - accuracy: 0.7360 - precision_1: 0.7360 - recall_1: 0.7360 - val_loss: 0.5435 - val_accuracy: 0.7594 - val_precision_1: 0.7594 - val_recall_1: 0.7594
Epoch 36/50
214/214 [==============================] - 16s 74ms/step - loss: 0.5731 - accuracy: 0.7646 - precision_1: 0.7646 - recall_1: 0.7646 - val_loss: 1.1972 - val_accuracy: 0.5500 - val_precision_1: 0.5500 - val_recall_1: 0.5500
Epoch 37/50
214/214 [==============================] - 16s 76ms/step - loss: 0.5438 - accuracy: 0.7654 - precision_1: 0.7654 - recall_1: 0.7654 - val_loss: 0.4254 - val_accuracy: 0.8375 - val_precision_1: 0.8375 - val_recall_1: 0.8375
Epoch 38/50
214/214 [==============================] - 16s 74ms/step - loss: 0.5089 - accuracy: 0.7914 - precision_1: 0.7914 - recall_1: 0.7914 - val_loss: 0.4077 - val_accuracy: 0.8250 - val_precision_1: 0.8250 - val_recall_1: 0.8250
Epoch 39/50
214/214 [==============================] - 16s 74ms/step - loss: 0.5575 - accuracy: 0.7578 - precision_1: 0.7578 - recall_1: 0.7578 - val_loss: 0.5978 - val_accuracy: 0.7063 - val_precision_1: 0.7063 - val_recall_1: 0.7063
Epoch 40/50
214/214 [==============================] - 16s 73ms/step - loss: 0.5380 - accuracy: 0.7650 - precision_1: 0.7650 - recall_1: 0.7650 - val_loss: 3.1841 - val_accuracy: 0.4531 - val_precision_1: 0.4531 - val_recall_1: 0.4531
Epoch 41/50
214/214 [==============================] - 16s 74ms/step - loss: 0.5357 - accuracy: 0.7563 - precision_1: 0.7563 - recall_1: 0.7563 - val_loss: 0.7997 - val_accuracy: 0.6781 - val_precision_1: 0.6781 - val_recall_1: 0.6781
Epoch 42/50
214/214 [==============================] - 16s 74ms/step - loss: 0.5256 - accuracy: 0.7789 - precision_1: 0.7789 - recall_1: 0.7789 - val_loss: 0.6315 - val_accuracy: 0.7719 - val_precision_1: 0.7719 - val_recall_1: 0.7719
Epoch 43/50
214/214 [==============================] - 14s 64ms/step - loss: 0.6169 - accuracy: 0.7166 - precision_1: 0.7166 - recall_1: 0.7166 - val_loss: 3.3632 - val_accuracy: 0.4781 - val_precision_1: 0.4781 - val_recall_1: 0.4781
Epoch 44/50
214/214 [==============================] - 17s 78ms/step - loss: 0.5972 - accuracy: 0.7483 - precision_1: 0.7483 - recall_1: 0.7483 - val_loss: 0.8131 - val_accuracy: 0.6469 - val_precision_1: 0.6469 - val_recall_1: 0.6469s - loss: 0.5724 - acc
Epoch 45/50
214/214 [==============================] - 13s 63ms/step - loss: 0.7085 - accuracy: 0.6755 - precision_1: 0.6755 - recall_1: 0.6755 - val_loss: 0.5101 - val_accuracy: 0.8219 - val_precision_1: 0.8219 - val_recall_1: 0.8219
Epoch 46/50
214/214 [==============================] - 14s 64ms/step - loss: 0.5875 - accuracy: 0.7264 - precision_1: 0.7264 - recall_1: 0.7264 - val_loss: 0.5257 - val_accuracy: 0.7500 - val_precision_1: 0.7500 - val_recall_1: 0.7500
Epoch 47/50
214/214 [==============================] - 14s 64ms/step - loss: 0.7334 - accuracy: 0.6632 - precision_1: 0.6632 - recall_1: 0.6632 - val_loss: 0.7317 - val_accuracy: 0.6125 - val_precision_1: 0.6125 - val_recall_1: 0.6125
Epoch 48/50
214/214 [==============================] - 14s 66ms/step - loss: 0.7614 - accuracy: 0.6407 - precision_1: 0.6407 - recall_1: 0.6407 - val_loss: 0.6236 - val_accuracy: 0.7188 - val_precision_1: 0.7188 - val_recall_1: 0.7188
Epoch 49/50
214/214 [==============================] - 14s 65ms/step - loss: 0.7781 - accuracy: 0.6605 - precision_1: 0.6605 - recall_1: 0.6605 - val_loss: 0.5485 - val_accuracy: 0.8156 - val_precision_1: 0.8156 - val_recall_1: 0.8156
Epoch 50/50
214/214 [==============================] - 14s 66ms/step - loss: 0.7112 - accuracy: 0.6897 - precision_1: 0.6897 - recall_1: 0.6897 - val_loss: 0.5975 - val_accuracy: 0.7750 - val_precision_1: 0.7750 - val_recall_1: 0.7750


fig = go.Figure(data=go.Scatter(
    y=history_model_InceptionV3.history["val_loss"],
    mode='lines+markers',
    marker=dict(size=np.array(history_model_InceptionV3.history["val_loss"]),
                color=np.array(history_model_InceptionV3.history["val_loss"])*50),
    name="IncptionV3"
))


fig.add_trace(go.Scatter(
    y=history_custom_model.history["val_loss"],
    mode='lines+markers',
    marker=dict(size=np.array(history_custom_model.history["val_loss"]),
                color=np.array(history_custom_model.history["val_loss"])*50),
    name="Custom Model"
))


fig.update_layout(title=go.layout.Title(text="Test Loss Comparison",
                                        font=go.layout.title.Font(size=25)))

fig.show()


# select real samples
def generate_real_samples(dataset, n_samples):
    # split into images and labels
    images, labels = dataset
    # choose random instances
    ix = np.random.randint(0, images.shape[0], n_samples)
    # select images and labels
    X, labels = images[ix]/255., labels[ix]
    # generate class labels
    y = np.ones((n_samples, 1))
    return [X, labels], y

# generate points in latent space as input for the generator
def generate_latent_points(latent_dim, n_samples):
    # generate points in the latent space
    z_input = np.random.randn(latent_dim * n_samples)
    # reshape into a batch of inputs for the network
    z_input = z_input.reshape(n_samples, latent_dim)
    return z_input

# use the generator to generate n fake examples, with class labels
def generate_fake_samples(generator, latent_dim, n_samples):
    # generate points in latent space
    z_input = generate_latent_points(latent_dim, n_samples)
    # predict outputs
    images = generator.predict(z_input)
    # create class labels
    y = np.zeros((n_samples, 1))
    return images, y

# custom activation function
def custom_activation(output):
    logexpsum = tf.keras.backend.sum(tf.keras.backend.exp(output), axis=-1, keepdims=True)
    result = logexpsum / (logexpsum + 1.0)
    return result

# define the standalone supervised and unsupervised discriminator models
def define_discriminator(in_shape=(128,128,3), n_classes=3):
    # image input
    in_image = tf.keras.layers.Input(shape=in_shape)
    # downsample
    fe = tf.keras.layers.Conv2D(64, (3,3), strides=(2,2), padding='same')(in_image)
    fe = tf.keras.layers.LeakyReLU(alpha=0.2)(fe)
    # downsample
    fe = tf.keras.layers.Conv2D(128, (3,3), strides=(2,2), padding='same')(fe)
    fe = tf.keras.layers.LeakyReLU(alpha=0.2)(fe)
    # downsample
    fe = tf.keras.layers.Conv2D(128, (3,3), strides=(2,2), padding='same')(fe)
    fe = tf.keras.layers.LeakyReLU(alpha=0.2)(fe)
    # downsample
    fe = tf.keras.layers.Conv2D(64, (3,3), strides=(2,2), padding='same')(fe)
    fe = tf.keras.layers.LeakyReLU(alpha=0.2)(fe)
    # flatten feature maps
    fe = tf.keras.layers.Flatten()(fe)
    # dropout
    fe = tf.keras.layers.Dropout(0.4)(fe)
    
    fe = tf.keras.layers.Dense(256)(fe)
    fe = tf.keras.layers.Dense(128)(fe)
    
    # output layer nodes
    c_out_layer = tf.keras.layers.Dense(n_classes)(fe)
    # supervised output
    c_out_layer = tf.keras.layers.Activation('softmax')(c_out_layer)
    # define and compile supervised discriminator model
    c_model = tf.keras.models.Model(in_image, c_out_layer)
    c_model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(lr=0.0002, beta_1=0.5), metrics=['accuracy',
                                                                                                                        tf.keras.metrics.Precision(top_k=1),
                                                                                                                        tf.keras.metrics.Recall(top_k=1)])
    
    # unsupervised output
    d_out_layer = tf.keras.layers.Lambda(custom_activation)(fe)
    # define and compile unsupervised discriminator model
    d_model = tf.keras.models.Model(in_image, d_out_layer)
    d_model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(lr=0.0002, beta_1=0.5))
    return d_model, c_model


# define the standalone generator model
def define_generator(latent_dim):
    # image generator input
    in_lat = tf.keras.layers.Input(shape=(latent_dim,))
    # foundation for 4x4 image
    n_nodes = 1024 * 4 * 4
    gen = tf.keras.layers.Dense(n_nodes)(in_lat)
    gen = tf.keras.layers.LeakyReLU(alpha=0.2)(gen)
    gen = tf.keras.layers.Reshape((4, 4, 1024))(gen)
    # upsample to 8x8
    gen = tf.keras.layers.Conv2DTranspose(128, (4,4), strides=(2,2), padding='same')(gen)
    gen = tf.keras.layers.LeakyReLU(alpha=0.2)(gen)
    # upsample to 16x16
    gen = tf.keras.layers.Conv2DTranspose(128, (4,4), strides=(2,2), padding='same')(gen)
    gen = tf.keras.layers.LeakyReLU(alpha=0.2)(gen)
    # upsample to 32x32
    gen = tf.keras.layers.Conv2DTranspose(128, (4,4), strides=(2,2), padding='same')(gen)
    gen = tf.keras.layers.LeakyReLU(alpha=0.2)(gen)
    # upsample to 64x64
    gen = tf.keras.layers.Conv2DTranspose(128, (4,4), strides=(2,2), padding='same')(gen)
    gen = tf.keras.layers.LeakyReLU(alpha=0.2)(gen)
    # upsample to 128x128
    gen = tf.keras.layers.Conv2DTranspose(128, (4,4), strides=(2,2), padding='same')(gen)
    gen = tf.keras.layers.LeakyReLU(alpha=0.2)(gen)
    # output
    out_layer = tf.keras.layers.Conv2D(3, (7,7), activation='tanh', padding='same')(gen)
    # define model
    model = tf.keras.models.Model(in_lat, out_layer)
    return model

# define the combined generator and discriminator model, for updating the generator
def define_gan(g_model, d_model):
    # make weights in the discriminator not trainable
    d_model.trainable = False
    # connect image output from generator as input to discriminator
    gan_output = d_model(g_model.output)
    # define gan model as taking noise and outputting a classification
    model = tf.keras.models.Model(g_model.input, gan_output)
    # compile model
    opt = tf.keras.optimizers.Adam(lr=0.0002, beta_1=0.5)
    model.compile(loss='binary_crossentropy', optimizer=opt)
    return model



# generate samples and save as a plot and save the model
def summarize_performance(step, g_model, c_model, latent_dim, dataset, n_samples=16):
    # prepare fake examples
    X, _ = generate_fake_samples(g_model, latent_dim, n_samples)
    # scale from [-1,1] to [0,1]
    X = (X + 1) / 2.0
    # plot images
    for i in range(n_samples):
        # define subplot
        plt.subplot(4, 4, 1 + i)
        # turn off axis
        plt.axis('off')
        # plot raw pixel data
        plt.imshow(X[i, :, :, :],)
    # save plot to file
    filename1 = 'logs/generated_plot_%04d.png' % (step+1)
    plt.savefig(filename1)
    plt.close()
    # evaluate the classifier model
    X, y = dataset
    X = X/255.
    loss, acc,precision,recall = c_model.evaluate(X, y, verbose=0)
    
    
    print('Classifier Accuracy: %.3f%%, Precision: %.3f%%, Recall: %.3f%%' % (acc * 100,precision,recall))
    
    if step %10 == 0:
        # save the generator model
        filename2 = 'logs/g_model_%04d.h5' % (step+1)
        g_model.save(filename2)
        # save the classifier model
        filename3 = 'logs/c_model_%04d.h5' % (step+1)
        c_model.save(filename3)
        print('>Saved: %s, %s, and %s' % (filename1, filename2, filename3))

    return loss,acc,precision,recall

# train the generator and discriminator
def train(g_model, d_model, c_model, gan_model, train_dataset,test_dataset, latent_dim, n_epochs=50, n_batch=6):

    # calculate the number of batches per training epoch
    bat_per_epo = int(train_dataset[0].shape[0] / n_batch)
    
    gan_loss,gan_acc,gan_precison,gan_recall = [],[],[],[]
    print('n_epochs=%d, bat_per_epo=%d' % (n_epochs, bat_per_epo))
    # manually enumerate epochs
    for epoch in tqdm(range(n_epochs),desc="EPOCHS =>"):
        for i in tqdm(range(bat_per_epo),desc="Training on Epoch {} =>".format(epoch+1)):
            # update supervised discriminator (c)
            [Xsup_real, ysup_real], _ = generate_real_samples(train_dataset, n_batch)
            c_loss, c_acc,_,_ = c_model.train_on_batch(Xsup_real, ysup_real)
            # update unsupervised discriminator (d)
            [X_real, _], y_real = generate_real_samples(train_dataset, n_batch)
            d_loss1 = d_model.train_on_batch(X_real, y_real)
            X_fake, y_fake = generate_fake_samples(g_model, latent_dim, n_batch)
            d_loss2 = d_model.train_on_batch(X_fake, y_fake)
            # update generator (g)
            X_gan, y_gan = generate_latent_points(latent_dim, n_batch), np.ones((n_batch, 1))
            g_loss = gan_model.train_on_batch(X_gan, y_gan)
            # summarize loss on this batch
            print('>%d, c[%.3f,%.0f], d[%.3f,%.3f], g[%.3f]' % (i+1, c_loss, c_acc*100, d_loss1, d_loss2, g_loss))
            # evaluate the model performance every so often
            
        loss,accuracy,precision,recall = summarize_performance(epoch, g_model, c_model, latent_dim, test_dataset)
        gan_loss.append(loss)
        gan_acc.append(accuracy)
        gan_precison.append(precision)
        gan_recall.append(recall)
    return gan_acc,gan_precison,gan_recall,gan_loss


X_train,X_test,Y_train,Y_test = train_test_split(images,labels_arr,test_size=0.2,stratify=labels_arr)


# create the discriminator models
d_model, c_model = define_discriminator(target_shape)
# create the generator
g_model = define_generator(latent_dim)
# create the gan
gan_model = define_gan(g_model, d_model)
# load image data
train_dataset = [X_train,Y_train]
test_dataset = [X_test,Y_test]
# train model
gan_acc,gan_precison,gan_recall,gan_loss = train(g_model, d_model, c_model, gan_model, train_dataset,test_dataset, latent_dim)

n_epochs=50, bat_per_epo=213

EPOCHS =>:   0%|                                                                                | 0/50 [00:00<?, ?it/s]
Training on Epoch 1 =>:   0%|                                                                  | 0/213 [00:00<?, ?it/s]
Training on Epoch 1 =>:   0%|▎                                                         | 1/213 [00:02<10:14,  2.90s/it]

>1, c[1.065,50], d[0.008,4.861], g[0.008]

Training on Epoch 1 =>:   1%|▌                                                         | 2/213 [00:03<04:48,  1.37s/it]

>2, c[1.122,17], d[0.008,4.859], g[0.008]

Training on Epoch 1 =>:   1%|▊                                                         | 3/213 [00:03<03:07,  1.12it/s]

>3, c[1.430,33], d[0.008,4.858], g[0.008]

Training on Epoch 1 =>:   2%|█                                                         | 4/213 [00:04<03:53,  1.12s/it]

>4, c[1.180,33], d[0.007,4.856], g[0.008]

Training on Epoch 1 =>:   2%|█▎                                                        | 5/213 [00:05<02:51,  1.21it/s]


gan_acc,gan_precison,gan_recall,gan_loss = np.array(gan_acc),np.array(gan_precison),np.array(gan_recall),np.array(gan_loss)


fig = go.Figure(data=go.Scatter(
    y=history_model_InceptionV3.history["val_accuracy"],
    mode='lines+markers',
    marker=dict(size=np.array(history_model_InceptionV3.history["val_accuracy"])*15,
                color=np.array(history_model_InceptionV3.history["val_accuracy"])*50),
    name="IncptionV3"
))


fig.add_trace(go.Scatter(
    y=history_custom_model.history["val_accuracy"],
    mode='lines+markers',
    marker=dict(size=np.array(history_custom_model.history["val_accuracy"])*15,
                color=np.array(history_custom_model.history["val_accuracy"])*50),
    name="Custom Model"
))

fig.add_trace(go.Scatter(
    y=gan_acc,
    mode='lines+markers',
    marker=dict(size=np.array(gan_acc)*15,
                color=np.array(gan_acc)*50),
    name="GAN Model"
))

fig.update_layout(title=go.layout.Title(text="Test Accuracy Comparison",
                                        font=go.layout.title.Font(size=25)))

fig.show()


fig = go.Figure(data=go.Scatter(
    y=history_model_InceptionV3.history["val_loss"],
    mode='lines+markers',
    marker=dict(size=np.array(history_model_InceptionV3.history["val_loss"]),
                color=np.array(history_model_InceptionV3.history["val_loss"])*50),
    name="IncptionV3"
))


fig.add_trace(go.Scatter(
    y=history_custom_model.history["val_loss"],
    mode='lines+markers',
    marker=dict(size=np.array(history_custom_model.history["val_loss"]),
                color=np.array(history_custom_model.history["val_loss"])*50),
    name="Custom Model"
))

fig.add_trace(go.Scatter(
    y=gan_loss,
    mode='lines+markers',
    marker=dict(size=np.array(gan_loss)*15,
                color=np.array(gan_loss)*50),
    name="GAN Model"
))


fig.update_layout(title=go.layout.Title(text="Test Loss Comparison",
                                        font=go.layout.title.Font(size=25)))

fig.show()


fig = go.Figure(data=go.Scatter(
    y=history_model_InceptionV3.history["precision"],
    mode='lines+markers',
    marker=dict(size=np.array(history_model_InceptionV3.history["precision"]),
                color=np.array(history_model_InceptionV3.history["precision"])*50),
    name="IncptionV3"
))


fig.add_trace(go.Scatter(
    y=history_custom_model.history["precision_1"],
    mode='lines+markers',
    marker=dict(size=np.array(history_custom_model.history["precision_1"]),
                color=np.array(history_custom_model.history["precision_1"])*50),
    name="Custom Model"
))

fig.add_trace(go.Scatter(
    y=gan_precison,
    mode='lines+markers',
    marker=dict(size=np.array(gan_precison)*15,
                color=np.array(gan_precison)*50),
    name="GAN Model"
))


fig.update_layout(title=go.layout.Title(text="Test Precision Comparison",
                                        font=go.layout.title.Font(size=25)))

fig.show()


fig = go.Figure(data=go.Scatter(
    y=history_model_InceptionV3.history["recall"],
    mode='lines+markers',
    marker=dict(size=np.array(history_model_InceptionV3.history["recall"]),
                color=np.array(history_model_InceptionV3.history["recall"])*50),
    name="IncptionV3"
))


fig.add_trace(go.Scatter(
    y=history_custom_model.history["recall_1"],
    mode='lines+markers',
    marker=dict(size=np.array(history_custom_model.history["recall_1"]),
                color=np.array(history_custom_model.history["recall_1"])*50),
    name="Custom Model"
))

fig.add_trace(go.Scatter(
    y=gan_recall,
    mode='lines+markers',
    marker=dict(size=np.array(gan_recall)*15,
                color=np.array(gan_recall)*50),
    name="GAN Model"
))


fig.update_layout(title=go.layout.Title(text="Test Recall Comparison",
                                        font=go.layout.title.Font(size=25)))

fig.show()

Importing libraries¶

Data Visualisation¶

Model comparative analysis¶

InceptionV3¶

Custom Model¶

GAN Model¶